A full action often consists of several sub-actions that may independently be detected in several overlapping proposals. Based on this observation, Zeng et al. in PGCN [1]} captured proposal-proposal relations by applying graph-convolution networks (GCNs). They constructed a graph where the nodes are the proposals. The edges connect highly overlapped proposals as well as disjoint but nearby proposals to provide contextual information. The edge weights model the relation between the proposals by measuring cosine similarity of their features. Through graph convolutions feature of each proposal gets updated by aggregating the information from other proposals. The updated features are then used to predict action categories, completeness, and refining the boundaries.
<FIGURE>